# Random ## Overview ![The Random Node.](/files/YCrt6kaPeK4ZO7HeHbSn) The **Random** **Node** generates a random outcome, usually a number. This **Node** can be set to three different `Modes` (**Advanced**, **Expert**, and **Standard**). Each of these `Modes` offers a different set of **Attributes** that are explained below. [**Scope**](/incari-studio/toolbox/overview.md#scopes): **Project**, **Scene**, **Function**, **Prefab**. ## Attributes ![The Random Node Attributes.](/files/hKt3kt2XKGnOizPY0QXy) Each `Mode` has a different set of **Attributes**. The `Modes` are: [**Advanced**](#advanced), [**Expert**](#expert), and [**Standard**](#standard). ### Advanced #### Generator This `Mode` allows the user to choose whether the random generator is deterministic or not, and for the deterministic case, the seed to use. | Attribute | Type | Description | | ------------------ | ----------------------------------------------------------------- | --------------------------------------------------------- | | `Is Deterministic` | **Bool** | Whether the random generator is deterministic or not. | | `Seed` | **Int** (*only available when `Is Deterministic` is set to true*) | The `Seed` to use for the deterministic random generator. | #### Distribution This **Mode** has a **Drop-down** menu from which the *probability distribution* used for the random generator can be chosen. Each option offers its own set of **Attributes** with the *probability distribution* parameters. | Attribute | Type | Description | | -------------- | ------------- | ------------------------------------------------------------------ | | `Distribution` | **Drop-down** | The *probability distribution* that the random generator will use. | Next, the **Attributes** for each *probability distribution* are described. For each *probability distribution*, the link to its corresponding Wikipedia entry is given. * [Bernoulli](https://en.wikipedia.org/wiki/Bernoulli_distribution) *Probability distribution* of a *random variable* that can take two values: *true*, with probability p; and *false*, with probability 1-p. When this distribution is chosen, the outcome of the **Node** is a **Boolean**. | Attribute | Type | Description | | ----------------------- | ----------------------------- | ------------------------------------------------ | | `Probability of 'true'` | **Float** (*between 0 and 1*) | The probability that the outcome will be *true*. | * [Binomial](https://en.wikipedia.org/wiki/Binomial_distribution) *Probability distribution* of the number of successes in a sequence of independent experiments, each one with two possible outcomes: success and failure. The parameters for this *probability distribution* are the number of experiments and the probability of a successful outcome in each one. | Attribute | Type | Description | | ----------------------- | ------------- | ---------------------------------------------------------------------------------------------------- | | `Data Type` | **Drop-down** | Whether the outcome will be an **Int** or **Byte**. | | `Probability of 'true'` | **Float** | The probability that the outcome of each trial is *true*. | | `Number of trials` | **Int** | The number of independent experiments, each one with probability of success `Probability of 'true'`. | * [Normal](https://en.wikipedia.org/wiki/Normal_distribution) Symmetric *probability distribution*, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation. | Attribute | Type | Description | | -------------------- | --------- | ------------------------------------------- | | `Mean` | **Float** | The mean value of the distribution. | | `Standard deviation` | **Float** | The standard deviation of the distribution. | * [Poisson](https://en.wikipedia.org/wiki/Poisson_distribution) Discrete *probability distribution* that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value. | Attribute | Type | Description | | ----------- | ------------- | -------------------------------------------------- | | `Data Type` | **Drop-down** | Wheter the outcome will be an **Int** or **Byte**. | | `Mean` | **Float** | The mean value of the distribution. | * Uniform *Probability distribution* in which all the values in an interval are equally likely to be drawn. It can either be [continuous](https://en.wikipedia.org/wiki/Continuous_uniform_distribution) or [discrete](https://en.wikipedia.org/wiki/Discrete_uniform_distribution). | Attribute | Type | Description | | ----------- | -------------------------------------------- | ------------------------------------------------------------------------------- | | `Data Type` | **Drop-down** | Whether an **Int**, **Float**, or **Byte** will be generated. | | `Minimum` | *Defined in the `Data Type`* ***Attribute*** | The lower bound of the interval from which the random number will be extracted. | | `Maximum` | *Defined in the `Data Type`* ***Attribute*** | The upper bound of the interval from which the random number will be extracted. | ### Expert #### Generator This `Mode` allows to choose from a list of several types of random generators. | Attribute | Type | Description | | ----------- | ------------------------------------------------------------ | ------------------------------------------- | | `Generator` | **Drop-down** | The type of random generator to use. | | `Seed` | **Int** (*not available for non\_deterministic `Generator`*) | The `Seed` to use for the random generator. | #### Distribution This `Mode` has a **Drop-down** menu from which the *probability distribution* to be used for the random generator can be chosen. Each option offers its own set of **Attributes** with the *probability distribution* parameters. | Attribute | Type | Description | | -------------- | ------------- | ------------------------------------------------------------------ | | `Distribution` | **Drop-down** | The *probability distribution* that the random generator will use. | Next, the **Attributes** for each *probability distribution* are described. For each *probability distribution*, the link to its corresponding Wikipedia entry is given. * [Bernoulli](https://en.wikipedia.org/wiki/Bernoulli_distribution) *Probability distribution* of a *random variable* that can take two values: *true*, with probability p; and *false*, with probability 1-p. When this distribution is chosen, the outcome of the **Node** is a **Boolean**. | Attribute | Type | Description | | ----------------------- | ----------------------------- | ------------------------------------------------ | | `Probability of 'true'` | **Float** (*between 0 and 1*) | The probability that the outcome will be *true*. | * [Binomial](https://en.wikipedia.org/wiki/Binomial_distribution) *Probability distribution* of the number of successes in a sequence of independent experiment, each one with two possible outcomes: success and failure. The parameters for this *probability distribution* are the number of experiments and the probability of a successful outcome in each one. | Attribute | Type | Description | | ----------------------- | ------------- | -------------------------------------------------------------------------------------------------------------- | | `Data Type` | **Drop-down** | Whether the outcome will be an **Int** or **Byte**. | | `Probability of 'true'` | **Float** | The probability that the outcome of each trial is *true*. | | `Number of trials` | **Int** | The number of independent experiments performed, each one with probability of success `Probability of 'true'`. | * [Cauchy](https://en.wikipedia.org/wiki/Cauchy_distribution) *Probability distribution* that resembles a *normal* distribution but with a taller peak, whose tails decay slower. Its parameters are the location of the peak and the scale - the latter defines its width. | Attribute | Type | Description | | ---------- | --------- | ------------------------------------------------------------------------------ | | `Location` | **Float** | Defines where the peak is. | | `Scale` | **Float** | Half the width of the probability density function at half the maximum height. | * [Chi\_Squared](https://en.wikipedia.org/wiki/Chi-squared_distribution) *Probability distribution* of a sum of the squares of a number of independent normal *random variables*. The number of normal *random variables* is called the degrees of freedom of the Chi-squared *distribution*. | Attribute | Type | Description | | -------------------- | --------- | ---------------------------------------------------------------- | | `Degrees of freedom` | **Float** | Number of independent normal *random variables* that are summed. | * [Exponential](https://en.wikipedia.org/wiki/Exponential_distribution) *Probability distribution* of the time between events in a [Poisson process](https://en.wikipedia.org/wiki/Poisson_point_process). Its parameter is the rate at which the events in the Poisson process occur. | Attribute | Type | Description | | --------- | --------- | ------------------------------------------------------ | | `Rate` | **Float** | Rate at which the events in the Poisson process occur. | * [Extreme\_Value](https://en.wikipedia.org/wiki/Generalized_extreme_value_distribution) Limit distribution of properly normalized maxima of a sequence of independent and identically distributed *random variables*. | Attribute | Type | Description | | ---------- | --------- | -------------------------------------- | | `Location` | **Float** | Defines where the peak is. | | `Scale` | **Float** | Defines how spread out the values are. | * [Fisher\_F](https://en.wikipedia.org/wiki/F-distribution) Ratio of two independent *random variables* with chi-squared distributions, each one divided by its corresponding number of degrees of freedom for scaling. | Attribute | Type | Description | | ----------------- | --------- | --------------------------------------------------------------------------- | | `Denominator Dof` | **Float** | Degrees of freedom of the chi-squared *random variable* in the denominator. | | `Numerator DoF` | **Float** | Degrees of freedom of the chi-squared *random variable* in the numerator. | * [Gamma](https://en.wikipedia.org/wiki/Gamma_distribution) Maximum entropy probability distribution for a *random variable*, whose mean is the product between the shape and scale, which are the two parameters of the Gamma distribution. | Attribute | Type | Description | | --------- | --------- | --------------------------------------------------- | | `Shape` | **Float** | Modifies the shape of the probability distribution. | | `Scale` | **Float** | Defines how spread out are the values. | * [Geometric](https://en.wikipedia.org/wiki/Geometric_distribution) The probability distribution of the number of experiments with a Bernoulli distribution needed to get one success. | Attribute | Type | Description | | ----------------------- | ----------------------------- | --------------------------------------------------- | | `Data Type` | **Drop-down** | Whether the output is an **Int** or **Byte**. | | `Probability of 'true'` | **Float** (*between 0 and 1*) | The probability of success in the Bernoulli trials. | * [Lognormal](https://en.wikipedia.org/wiki/Log-normal_distribution) Probability distribution of a *random variable* whose logarithm has a normal distribution. | Attribute | Type | Description | | -------------------- | --------- | ------------------------------------------------------------ | | `Mean` | **Float** | The mean value of the logarithm of the distribution. | | `Standard deviation` | **Float** | The standard deviation of the logarithm of the distribution. | * [Negative\_Binomial](https://en.wikipedia.org/wiki/Negative_binomial_distribution) *Probability distribution* of the number of successes in a sequence of independent experiments, each with two possible outcomes: success and failure, before a specified non-random number of failures occur. The parameters for this *probability distribution* are the probability of a successful outcome in each experiment and the number of failures until the experiments stop. | Attribute | Type | Description | | ----------------------- | ----------------------------- | ----------------------------------------------------------- | | `Data Type` | **Drop-down** | Whether the outcome is an **Int** or **Byte**. | | `Probability of 'true'` | **Float** (*between 0 and 1*) | The probability that the outcome of each trial is *true*. | | `Number of trials` | **Int** | The number of failures to occur until the experiments stop. | * [Normal](https://en.wikipedia.org/wiki/Normal_distribution) Symmetric *probability distribution*, with half its values less than the mean and half greater than the mean. The parameters are the mean, which equals the median and the mode, and the standard deviation. | Attribute | Type | Description | | -------------------- | --------- | ------------------------------------------- | | `Mean` | **Float** | The mean value of the distribution. | | `Standard deviation` | **Float** | The standard deviation of the distribution. | * [Poisson](https://en.wikipedia.org/wiki/Poisson_distribution) Discrete *probability distribution* that expresses the probability of a given number of events occurring in a specified time period. Its parameter is the mean value. | Attribute | Type | Description | | ----------- | ------------- | -------------------------------------------------- | | `Data Type` | **Drop-down** | Wheter the outcome will be an **Int** or **Byte**. | | `Mean` | **Float** | The mean value of the distribution. | * [Student\_T](https://en.wikipedia.org/wiki/Student%27s_t-distribution) *Probability distribution* that arises when estimating the mean of a normally-distributed statistical population with a small sample size and unknown standard deviation. Its parameter is the number of degrees of freedom, which is the number of observations taken from a normal distribution minus one. | Attribute | Type | Description | | -------------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | `Degrees of freedom` | **Float** | The number of observations taken from a normal distribution minus one. As it grows, the Student-t distribution approaches a normal distribution. | * Uniform *Probability distribution* in which all the values in an interval are equally likely to be drawn. It can either be [continuous](https://en.wikipedia.org/wiki/Continuous_uniform_distribution) or [discrete](https://en.wikipedia.org/wiki/Discrete_uniform_distribution). | Attribute | Type | Description | | ----------- | -------------------------------------------- | ------------------------------------------------------------------------------- | | `Data Type` | **Drop-down** | Whether an **Int**, **Float**, or **Byte** will be generated. | | `Minimum` | *Defined in the `Data Type`* ***Attribute*** | The lower bound of the interval from which the random number will be extracted. | | `Maximum` | *Defined in the `Data Type`* ***Attribute*** | The upper bound of the interval from which the random number will be extracted. | * [Weibull](https://en.wikipedia.org/wiki/Weibull_distribution) | Attribute | Type | Description | | --------- | --------- | ---------------------------------------------------------------------- | | `Shape` | **Float** | Defines the shape of the probability distribution. | | `Scale` | **Float** | Defines how spread out the values of the probability distribution are. | ### Standard This `Mode` only uses a uniform distribution. It can either be [discrete](https://en.wikipedia.org/wiki/Discrete_uniform_distribution) or [continuous](https://en.wikipedia.org/wiki/Continuous_uniform_distribution). #### Distribution | Attribute | Type | Description | | ----------- | -------------------------------------------- | ------------------------------------------------------------------------------- | | `Data Type` | **Drop-down** | Whether an **Int**, **Float**, or **Byte** will be generated. | | `Minimum` | *Defined in the `Data Type`* ***Attribute*** | The lower bound of the interval from which the random number will be extracted. | | `Maximum` | *Defined in the `Data Type`* ***Attribute*** | The upper bound of the interval from which the random number will be extracted. | ## Inputs | Input | Type | Description | | ----------------- | --------- | --------------------------------------------------------------------- | | *Pulse Input* (►) | **Pulse** | A standard **Input Pulse**, to trigger the execution of the **Node**. | ## Outputs | Output | Type | Description | | ------------------ | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------- | | *Pulse Output* (►) | **Pulse** | A standard **Output Pulse**, to move onto the next **Node** along the **Logic Branch**, once this **Node** has finished its execution. | | `Output` | *Depends on the `Mode` and `Distribution`* | The random outcome that was generated. | ## External Links * [*Random number generation*](https://en.wikipedia.org/wiki/Random_number_generation) on Wikipedia. * [*Pseudorandom number generator*](https://en.wikipedia.org/wiki/Pseudorandom_number_generator) on Wikipedia. * [*List of probability distributions*](https://en.wikipedia.org/wiki/List_of_probability_distributions) on Wikipedia. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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